Abstract

In recent years, microelectromechanical system (MEMS)
inertial sensors (3D accelerometers and 3D gyroscopes) have
become widely available due to their small size and low
cost. Inertial sensor measurements are obtained at high
sampling rates and can be integrated to obtain position and
orientation information. These estimates are accurate on
a short time scale, but suffer from integration drift over
longer time scales. To overcome this issue, inertial sensors
are typically combined with additional sensors and models.
In this tutorial we focus on the signal processing aspects of
position and orientation estimation using inertial sensors.We
discuss different modeling choices and a selected number of
important algorithms. The algorithms include optimizationbased
smoothing and filtering as well as computationally
cheaper extended Kalman filter and complementary filter
implementations. The quality of their estimates is illustrated
using both experimental and simulated data.

Using Inertial Sensors for Position and Orientation Estimation

Microelectromechanical system (MEMS) inertial sensors have become ubiquitous in modern society. Built into mobile telephones, gaming consoles, virtual reality headsets, we use such sensors on a daily basis. They also have applications in medical therapy devices, motion-capture filming, traffic monitoring systems, and drones. While providing accurate measurements over short time scales, this diminishes over longer periods. To date, this problem has been resolved by combining them with additional sensors and models.
This adds both expense and size to the devices.

This tutorial focuses on the signal processing aspects of position and orientation estimation using inertial sensors. It discusses different modelling choices and a selected number of important algorithms that engineers can use to select the best options for their designs. The algorithms include optimization-based smoothing and
filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations.

Engineers, researchers, and students deploying MEMS inertial sensors will find that this tutorial is an essential monograph on how to optimize their designs.